Many images are produced using computerized methods that do not rely on a pixel-based representation of an image. Text processing software, for instance, represents an image using structured page information that describes high-level elements of the image, such as text, fonts, colors, embedded images, etc. This structured page information comes in a large variety of file formats such as MSWord™ doc, Adobe™ PDF, or PostScript™ files. When printed or otherwise rendered, the information may be converted into a sequence of overlaid image elements that incrementally construct the image.
There is often a need to compress, i.e., encode, these images. Generally, the image elements are first classified as either foreground or background based on some classification criteria. After classification, the foreground is encoded at a higher resolution because it contains the elements of interest. The background, on the other hand, is typically encoded at a lower resolution since it contains elements of less interest. Such a coding strategy is well known in the art, e.g. in MPEG, JPEG, etc. Thus, the quality of the element classification greatly affects the compression ratio and video quality of these images. As such, it is important to perform the classification effectively.
Current element classification approaches for images rendered from structured page information include classifying all the text in the image as the foreground and all other details as the background, classifying all the monochrome elements as the foreground and all others as the background, and classifying the first element drawn as the background and all others as the foreground. However, all of these approaches are ineffective, particularly for geographical maps, because the elements of interest are sometimes rendered such that they meet the criteria for background classification when, in fact, they are foreground elements. As a result, these elements of interest are erroneously encoded at a lower resolution. As such, the compression efficiency and video quality of these elements significantly drop.
Accordingly, there is a need in the art for an effective way to classify image elements, in general, and image elements rendered from structured page information, e.g., electronic documents, in particular.